An experimentally supported model of the Bacillus subtilis global transcriptional regulatory network

• Published in: Molecular systems biology Vol. 11; no. 11; pp. 839 - n/a
• Main Authors: Arrieta‐Ortiz, Mario L, Hafemeister, Christoph, Bate, Ashley Rose, Chu, Timothy, Greenfield, Alex, Shuster, Bentley, Barry, Samantha N, Gallitto, Matthew, Liu, Brian, Kacmarczyk, Thadeous, Santoriello, Francis, Chen, Jie, Rodrigues, Christopher DA, Sato, Tsutomu, Rudner, David Z, Driks, Adam, Bonneau, Richard, Eichenberger, Patrick
• Format: Journal Article
• Language: English
• Published: England EMBO Press 01.11.2015; John Wiley and Sons Inc
• Subjects: Bacillus subtilis; Bacillus subtilis - genetics; Binding sites; Bioengineering; Biology; Databases, Genetic; Datasets; Deoxyribonucleic acid; DNA; Domains; E coli; Environmental regulations; Experiments; Gene expression; Gene Expression Regulation, Bacterial - genetics; Gene regulation; Gene Regulatory Networks - genetics; Genes, Bacterial - genetics; Genomes; Metabolism; Metabolites; Microscopy; Models, Genetic; network inference; RNA polymerase; Software; Spores, Bacterial - genetics; sporulation; Systems Biology; Transcription factors; transcriptional networks; Transcriptome - genetics; network inference; Bacillus subtilis; transcriptional networks; sporulation
• ISSN: 1744-4292; 1744-4292
• DOI: 10.15252/msb.20156236

Organisms from all domains of life use gene regulation networks to control cell growth, identity, function, and responses to environmental challenges. Although accurate global regulatory models would provide critical evolutionary and functional insights, they remain incomplete, even for the best studied organisms. Efforts to build comprehensive networks are confounded by challenges including network scale, degree of connectivity, complexity of organism–environment interactions, and difficulty of estimating the activity of regulatory factors. Taking advantage of the large number of known regulatory interactions in Bacillus subtilis and two transcriptomics datasets (including one with 38 separate experiments collected specifically for this study), we use a new combination of network component analysis and model selection to simultaneously estimate transcription factor activities and learn a substantially expanded transcriptional regulatory network for this bacterium. In total, we predict 2,258 novel regulatory interactions and recall 74% of the previously known interactions. We obtained experimental support for 391 (out of 635 evaluated) novel regulatory edges (62% accuracy), thus significantly increasing our understanding of various cell processes, such as spore formation. Synopsis A new computational framework integrating network component analysis and model selection is combined with transcriptomic datasets and generates an expanded and more accurate transcriptional regulatory network (TRN) for Bacillus subtilis. A global TRN is inferred for B. subtilis and contains 3,086 protein‐coding genes, 215 transcription factors (TFs) and predicts 4,516 interactions (2,258 novel). Previously known interactions are recalled at high proportion (74%) and experimental support is provided for 1,289 TF–gene interactions (out of 1,841 tested) in transcriptional profiling data with KO strains, including 391 (out of 635) novel interactions. The inferred TRN provides novel functional insights even for well‐studied pathways, such as spore formation. A new computational framework integrating network component analysis and model selection is combined with transcriptomic datasets and generates an expanded and more accurate transcriptional regulatory network (TRN) for Bacillus subtilis.